🐛 Describe the bug

🐛 Describe the bug

The docstring example in scaled_dot_product_attention contains a redundant and ineffective dtype conversion.

Code location:
torch/nn/functional.py - in the docstring of scaled_dot_product_attention

Issue:

attn_bias = torch.zeros(L, S, dtype=query.dtype, device=query.device)  # Already query.dtype
if is_causal:
    # ...
    attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf"))
    attn_bias.to(query.dtype)  # Redundant! Already query.dtype, and result not assigned

Problems:

1. attn_bias is already created with dtype=query.dtype
2. masked_fill_ is an in-place operation that doesn't change dtype
3. attn_bias.to(query.dtype) is redundant since it's already the correct dtype
4. The result of .to() is not assigned back, making it completely ineffective

Expected fix:
Remove the line attn_bias.to(query.dtype) entirely.

Versions

PyTorch version: 2.6.0+cu126
Is debug build: False
CUDA used to build PyTorch: 12.6
ROCM used to build PyTorch: N/A

OS: Rocky Linux 9.4 (Blue Onyx) (aarch64)
GCC version: (GCC) 11.4.1 20231218 (Red Hat 11.4.1-3)
Clang version: 17.0.6 (RESF 17.0.6-5.el9)
CMake version: version 3.26.5
Libc version: glibc-2.34

Python version: 3.10.16 (main, Dec 6 2024, 11:22:39) [GCC 11.4.1 20231218 (Red Hat 11.4.1-3)] (64-bit runtime)
Python platform: Linux-5.14.0-427.13.1.el9_4.aarch64+64k-aarch64-with-glibc2.34
Is CUDA available: True
CUDA runtime version: 12.6.85
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GH200 120GB
Nvidia driver version: 550.163.01
cuDNN version: Could not collect
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture: aarch64
CPU op-mode(s): 64-bit
Byte Order: Little Endian
CPU(s): 72
On-line CPU(s) list: 0-71
Vendor ID: ARM
Model name: Neoverse-V2
Model: 0
Thread(s) per core: 1
Core(s) per socket: 72
Socket(s): 1
Stepping: r0p0
Frequency boost: disabled
CPU(s) scaling MHz: 100%
CPU max MHz: 3402.0000
CPU min MHz: 81.0000
BogoMIPS: 2000.00
Flags: fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh
L1d cache: 4.5 MiB (72 instances)
L1i cache: 4.5 MiB (72 instances)
L2 cache: 72 MiB (72 instances)
L3 cache: 114 MiB (1 instance)
NUMA node(s): 9
NUMA node0 CPU(s): 0-71
NUMA node1 CPU(s):
NUMA node2 CPU(s):
NUMA node3 CPU(s):
NUMA node4 CPU(s):
NUMA node5 CPU(s):
NUMA node6 CPU(s):
NUMA node7 CPU(s):
NUMA node8 CPU(s):
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; __user pointer sanitization
Vulnerability Spectre v2: Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

Versions of relevant libraries:
[pip3] ema-pytorch==0.7.7
[pip3] numpy==2.1.3
[pip3] nvidia-cublas-cu12==12.5.3.2
[pip3] nvidia-cuda-cupti-cu12==12.5.82
[pip3] nvidia-cuda-nvrtc-cu12==12.5.82
[pip3] nvidia-cuda-runtime-cu12==12.5.82
[pip3] nvidia-cudnn-cu12==9.3.0.75
[pip3] nvidia-cufft-cu12==11.2.3.61
[pip3] nvidia-curand-cu12==10.3.6.82
[pip3] nvidia-cusolver-cu12==11.6.3.83
[pip3] nvidia-cusparse-cu12==12.5.1.3
[pip3] nvidia-nccl-cu12==2.23.4
[pip3] nvidia-nvjitlink-cu12==12.5.82
[pip3] optree==0.16.0
[pip3] torch==2.6.0+cu126
[pip3] torchaudio==2.6.0
[pip3] torchvision==0.21.0
[conda] Could not collect